Overview

Dataset statistics

Number of variables41
Number of observations233154
Missing cells7661
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.9 MiB
Average record size in memory328.0 B

Variable types

Numeric28
Categorical13

Alerts

MobileNo_Avl_Flag has constant value "1" Constant
Date.of.Birth has a high cardinality: 15433 distinct values High cardinality
DisbursalDate has a high cardinality: 84 distinct values High cardinality
AVERAGE.ACCT.AGE has a high cardinality: 192 distinct values High cardinality
CREDIT.HISTORY.LENGTH has a high cardinality: 294 distinct values High cardinality
UniqueID is highly correlated with DisbursalDateHigh correlation
disbursed_amount is highly correlated with asset_costHigh correlation
asset_cost is highly correlated with disbursed_amountHigh correlation
ltv is highly correlated with MobileNo_Avl_FlagHigh correlation
branch_id is highly correlated with Current_pincode_ID and 1 other fieldsHigh correlation
supplier_id is highly correlated with MobileNo_Avl_FlagHigh correlation
manufacturer_id is highly correlated with MobileNo_Avl_FlagHigh correlation
Current_pincode_ID is highly correlated with branch_id and 1 other fieldsHigh correlation
State_ID is highly correlated with branch_id and 3 other fieldsHigh correlation
Employee_code_ID is highly correlated with MobileNo_Avl_FlagHigh correlation
PERFORM_CNS.SCORE is highly correlated with PERFORM_CNS.SCORE.DESCRIPTIONHigh correlation
PRI.NO.OF.ACCTS is highly correlated with PRI.ACTIVE.ACCTS and 1 other fieldsHigh correlation
PRI.ACTIVE.ACCTS is highly correlated with PERFORM_CNS.SCORE.DESCRIPTION and 5 other fieldsHigh correlation
PRI.OVERDUE.ACCTS is highly correlated with MobileNo_Avl_FlagHigh correlation
PRI.CURRENT.BALANCE is highly correlated with PRI.ACTIVE.ACCTS and 3 other fieldsHigh correlation
PRI.SANCTIONED.AMOUNT is highly correlated with PRI.ACTIVE.ACCTS and 3 other fieldsHigh correlation
PRI.DISBURSED.AMOUNT is highly correlated with PRI.ACTIVE.ACCTS and 3 other fieldsHigh correlation
SEC.NO.OF.ACCTS is highly correlated with SEC.ACTIVE.ACCTS and 1 other fieldsHigh correlation
SEC.ACTIVE.ACCTS is highly correlated with SEC.NO.OF.ACCTS and 4 other fieldsHigh correlation
SEC.OVERDUE.ACCTS is highly correlated with SEC.NO.OF.ACCTS and 1 other fieldsHigh correlation
SEC.CURRENT.BALANCE is highly correlated with SEC.ACTIVE.ACCTS and 2 other fieldsHigh correlation
SEC.SANCTIONED.AMOUNT is highly correlated with SEC.ACTIVE.ACCTS and 2 other fieldsHigh correlation
SEC.DISBURSED.AMOUNT is highly correlated with SEC.ACTIVE.ACCTS and 2 other fieldsHigh correlation
PRIMARY.INSTAL.AMT is highly correlated with PRI.NO.OF.ACCTS and 4 other fieldsHigh correlation
SEC.INSTAL.AMT is highly correlated with SEC.NO.OF.ACCTS and 4 other fieldsHigh correlation
NEW.ACCTS.IN.LAST.SIX.MONTHS is highly correlated with PRI.NO.OF.ACCTS and 4 other fieldsHigh correlation
DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS is highly correlated with MobileNo_Avl_FlagHigh correlation
NO.OF_INQUIRIES is highly correlated with MobileNo_Avl_FlagHigh correlation
Employment.Type is highly correlated with MobileNo_Avl_FlagHigh correlation
DisbursalDate is highly correlated with UniqueIDHigh correlation
MobileNo_Avl_Flag is highly correlated with Driving_flag and 8 other fieldsHigh correlation
Aadhar_flag is highly correlated with State_ID and 1 other fieldsHigh correlation
PAN_flag is highly correlated with MobileNo_Avl_FlagHigh correlation
VoterID_flag is highly correlated with State_ID and 1 other fieldsHigh correlation
Driving_flag is highly correlated with MobileNo_Avl_FlagHigh correlation
Passport_flag is highly correlated with MobileNo_Avl_FlagHigh correlation
PERFORM_CNS.SCORE.DESCRIPTION is highly correlated with PERFORM_CNS.SCORE and 1 other fieldsHigh correlation
loan_default is highly correlated with MobileNo_Avl_FlagHigh correlation
Employment.Type has 7661 (3.3%) missing values Missing
PRI.CURRENT.BALANCE is highly skewed (γ1 = 29.42581325) Skewed
PRI.SANCTIONED.AMOUNT is highly skewed (γ1 = 323.6972121) Skewed
PRI.DISBURSED.AMOUNT is highly skewed (γ1 = 322.5414945) Skewed
SEC.NO.OF.ACCTS is highly skewed (γ1 = 27.98609032) Skewed
SEC.ACTIVE.ACCTS is highly skewed (γ1 = 30.59951015) Skewed
SEC.OVERDUE.ACCTS is highly skewed (γ1 = 24.12927125) Skewed
SEC.CURRENT.BALANCE is highly skewed (γ1 = 108.5062952) Skewed
SEC.SANCTIONED.AMOUNT is highly skewed (γ1 = 75.25493196) Skewed
SEC.DISBURSED.AMOUNT is highly skewed (γ1 = 75.76425191) Skewed
PRIMARY.INSTAL.AMT is highly skewed (γ1 = 69.91615647) Skewed
SEC.INSTAL.AMT is highly skewed (γ1 = 153.8063689) Skewed
UniqueID has unique values Unique
PERFORM_CNS.SCORE has 116950 (50.2%) zeros Zeros
PRI.NO.OF.ACCTS has 116950 (50.2%) zeros Zeros
PRI.ACTIVE.ACCTS has 137016 (58.8%) zeros Zeros
PRI.OVERDUE.ACCTS has 206879 (88.7%) zeros Zeros
PRI.CURRENT.BALANCE has 141696 (60.8%) zeros Zeros
PRI.SANCTIONED.AMOUNT has 138096 (59.2%) zeros Zeros
PRI.DISBURSED.AMOUNT has 138204 (59.3%) zeros Zeros
SEC.NO.OF.ACCTS has 227289 (97.5%) zeros Zeros
SEC.ACTIVE.ACCTS has 229337 (98.4%) zeros Zeros
SEC.OVERDUE.ACCTS has 231817 (99.4%) zeros Zeros
SEC.CURRENT.BALANCE has 229790 (98.6%) zeros Zeros
SEC.SANCTIONED.AMOUNT has 229418 (98.4%) zeros Zeros
SEC.DISBURSED.AMOUNT has 229450 (98.4%) zeros Zeros
PRIMARY.INSTAL.AMT has 159517 (68.4%) zeros Zeros
SEC.INSTAL.AMT has 230937 (99.0%) zeros Zeros
NEW.ACCTS.IN.LAST.SIX.MONTHS has 181494 (77.8%) zeros Zeros
DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS has 214959 (92.2%) zeros Zeros
NO.OF_INQUIRIES has 201961 (86.6%) zeros Zeros

Reproduction

Analysis started2022-11-01 12:07:25.599050
Analysis finished2022-11-01 12:14:22.309402
Duration6 minutes and 56.71 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

UniqueID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct233154
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean535917.5734
Minimum417428
Maximum671084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:22.541178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum417428
5-th percentile429285.65
Q1476786.25
median535978.5
Q3595039.75
95-th percentile642344.35
Maximum671084
Range253656
Interquartile range (IQR)118253.5

Descriptive statistics

Standard deviation68315.69371
Coefficient of variation (CV)0.1274742556
Kurtosis-1.198283034
Mean535917.5734
Median Absolute Deviation (MAD)59127
Skewness-0.002261591332
Sum1.249513259 × 1011
Variance4667034007
MonotonicityNot monotonic
2022-11-01T17:44:22.780794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4208251
 
< 0.1%
5733901
 
< 0.1%
4435791
 
< 0.1%
6344111
 
< 0.1%
4973401
 
< 0.1%
6131621
 
< 0.1%
6510431
 
< 0.1%
4439271
 
< 0.1%
4305821
 
< 0.1%
4372461
 
< 0.1%
Other values (233144)233144
> 99.9%
ValueCountFrequency (%)
4174281
< 0.1%
4174291
< 0.1%
4174301
< 0.1%
4174311
< 0.1%
4174321
< 0.1%
4174331
< 0.1%
4174341
< 0.1%
4174351
< 0.1%
4174361
< 0.1%
4174371
< 0.1%
ValueCountFrequency (%)
6710841
< 0.1%
6710331
< 0.1%
6586761
< 0.1%
6586751
< 0.1%
6586741
< 0.1%
6586731
< 0.1%
6586721
< 0.1%
6586711
< 0.1%
6586701
< 0.1%
6586691
< 0.1%

disbursed_amount
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24565
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54356.99353
Minimum13320
Maximum990572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:24.128896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13320
5-th percentile34939
Q147145
median53803
Q360413
95-th percentile74122.35
Maximum990572
Range977252
Interquartile range (IQR)13268

Descriptive statistics

Standard deviation12971.31417
Coefficient of variation (CV)0.2386319281
Kurtosis249.9892735
Mean54356.99353
Median Absolute Deviation (MAD)6644
Skewness4.492239664
Sum1.267355047 × 1010
Variance168254991.3
MonotonicityNot monotonic
2022-11-01T17:44:24.396783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
483492140
 
0.9%
533032125
 
0.9%
513031992
 
0.9%
503031960
 
0.8%
552591887
 
0.8%
523031868
 
0.8%
473491853
 
0.8%
562591637
 
0.7%
463491605
 
0.7%
572591601
 
0.7%
Other values (24555)214486
92.0%
ValueCountFrequency (%)
133201
 
< 0.1%
133691
 
< 0.1%
136001
 
< 0.1%
136401
 
< 0.1%
136521
 
< 0.1%
136646
< 0.1%
138141
 
< 0.1%
139141
 
< 0.1%
139401
 
< 0.1%
139411
 
< 0.1%
ValueCountFrequency (%)
9905721
< 0.1%
9873541
< 0.1%
5924601
< 0.1%
3320451
< 0.1%
3185331
< 0.1%
3159041
< 0.1%
2377791
< 0.1%
1969981
< 0.1%
1913921
< 0.1%
1908871
< 0.1%

asset_cost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46252
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75865.06814
Minimum37000
Maximum1628992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:24.642905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37000
5-th percentile58264
Q165717
median70946
Q379201.75
95-th percentile109680
Maximum1628992
Range1591992
Interquartile range (IQR)13484.75

Descriptive statistics

Standard deviation18944.78129
Coefficient of variation (CV)0.24971679
Kurtosis291.4939393
Mean75865.06814
Median Absolute Deviation (MAD)6200
Skewness6.133485336
Sum1.76882441 × 1010
Variance358904738.1
MonotonicityNot monotonic
2022-11-01T17:44:24.881910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68000681
 
0.3%
67000596
 
0.3%
72000539
 
0.2%
70000505
 
0.2%
74000469
 
0.2%
66000466
 
0.2%
73000462
 
0.2%
75000460
 
0.2%
69000436
 
0.2%
65000397
 
0.2%
Other values (46242)228143
97.9%
ValueCountFrequency (%)
370002
< 0.1%
371291
< 0.1%
372301
< 0.1%
373101
< 0.1%
373771
< 0.1%
376581
< 0.1%
378161
< 0.1%
380552
< 0.1%
380591
< 0.1%
380631
< 0.1%
ValueCountFrequency (%)
16289921
< 0.1%
13289541
< 0.1%
7151861
< 0.1%
4596251
< 0.1%
3880251
< 0.1%
3836001
< 0.1%
3780922
< 0.1%
2863501
< 0.1%
2826001
< 0.1%
2811641
< 0.1%

ltv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6579
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.74653002
Minimum10.03
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:25.126111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.03
5-th percentile52.31
Q168.88
median76.8
Q383.67
95-th percentile89.38
Maximum95
Range84.97
Interquartile range (IQR)14.79

Descriptive statistics

Standard deviation11.45663574
Coefficient of variation (CV)0.1532731451
Kurtosis1.29392765
Mean74.74653002
Median Absolute Deviation (MAD)7.27
Skewness-1.075766064
Sum17427452.46
Variance131.2545025
MonotonicityNot monotonic
2022-11-01T17:44:25.332931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
854430
 
1.9%
84.991030
 
0.4%
79.99541
 
0.2%
80490
 
0.2%
75420
 
0.2%
79.9409
 
0.2%
79.79394
 
0.2%
74.93389
 
0.2%
90336
 
0.1%
74.99333
 
0.1%
Other values (6569)224382
96.2%
ValueCountFrequency (%)
10.031
< 0.1%
13.51
< 0.1%
14.171
< 0.1%
15.31
< 0.1%
15.581
< 0.1%
16.61
< 0.1%
17.021
< 0.1%
17.051
< 0.1%
17.131
< 0.1%
17.361
< 0.1%
ValueCountFrequency (%)
958
 
< 0.1%
94.997
 
< 0.1%
94.989
< 0.1%
94.975
 
< 0.1%
94.9611
< 0.1%
94.9514
< 0.1%
94.9414
< 0.1%
94.9320
< 0.1%
94.9217
< 0.1%
94.9113
< 0.1%

branch_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.93609374
Minimum1
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:25.558624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median61
Q3130
95-th percentile249
Maximum261
Range260
Interquartile range (IQR)116

Descriptive statistics

Standard deviation69.83499455
Coefficient of variation (CV)0.9574819677
Kurtosis0.2970743025
Mean72.93609374
Median Absolute Deviation (MAD)50
Skewness1.027481357
Sum17005342
Variance4876.926464
MonotonicityNot monotonic
2022-11-01T17:44:25.810827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213138
 
5.6%
6711328
 
4.9%
39230
 
4.0%
59218
 
4.0%
368832
 
3.8%
1367833
 
3.4%
347794
 
3.3%
166466
 
2.8%
195860
 
2.5%
15709
 
2.4%
Other values (72)147746
63.4%
ValueCountFrequency (%)
15709
2.4%
213138
5.6%
39230
4.0%
59218
4.0%
73222
 
1.4%
83146
 
1.3%
92528
 
1.1%
104125
 
1.8%
114506
 
1.9%
132972
 
1.3%
ValueCountFrequency (%)
261176
 
0.1%
260372
 
0.2%
259346
 
0.1%
258374
 
0.2%
2571256
 
0.5%
2551650
0.7%
2541699
0.7%
2513844
1.6%
2501481
 
0.6%
249858
 
0.4%

supplier_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2953
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19638.63504
Minimum10524
Maximum24803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:26.042117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10524
5-th percentile14179.3
Q116535
median20333
Q323000
95-th percentile24122
Maximum24803
Range14279
Interquartile range (IQR)6465

Descriptive statistics

Standard deviation3491.949566
Coefficient of variation (CV)0.1778101971
Kurtosis-1.475599607
Mean19638.63504
Median Absolute Deviation (MAD)3061
Skewness-0.1689082748
Sum4578826313
Variance12193711.77
MonotonicityNot monotonic
2022-11-01T17:44:26.288879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183171432
 
0.6%
156941300
 
0.6%
156631275
 
0.5%
179801268
 
0.5%
142341258
 
0.5%
181661210
 
0.5%
219801125
 
0.5%
143751119
 
0.5%
227271062
 
0.5%
141451060
 
0.5%
Other values (2943)221045
94.8%
ValueCountFrequency (%)
105246
 
< 0.1%
123113
 
< 0.1%
1231246
< 0.1%
1237499
< 0.1%
1244147
< 0.1%
1245672
< 0.1%
1250060
< 0.1%
1253460
< 0.1%
125399
 
< 0.1%
1279765
< 0.1%
ValueCountFrequency (%)
248032
< 0.1%
248022
< 0.1%
247991
 
< 0.1%
247972
< 0.1%
247941
 
< 0.1%
247931
 
< 0.1%
247901
 
< 0.1%
247891
 
< 0.1%
247872
< 0.1%
247853
< 0.1%

manufacturer_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.02805442
Minimum45
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:26.565440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile45
Q148
median86
Q386
95-th percentile86
Maximum156
Range111
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.14130384
Coefficient of variation (CV)0.3207580457
Kurtosis-0.7192530333
Mean69.02805442
Median Absolute Deviation (MAD)34
Skewness0.3889804687
Sum16094167
Variance490.2373356
MonotonicityNot monotonic
2022-11-01T17:44:26.736371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
86109534
47.0%
4556626
24.3%
5127204
 
11.7%
4816710
 
7.2%
4910220
 
4.4%
1209658
 
4.1%
672405
 
1.0%
145778
 
0.3%
15312
 
< 0.1%
1526
 
< 0.1%
ValueCountFrequency (%)
4556626
24.3%
4816710
 
7.2%
4910220
 
4.4%
5127204
 
11.7%
672405
 
1.0%
86109534
47.0%
1209658
 
4.1%
145778
 
0.3%
1526
 
< 0.1%
15312
 
< 0.1%
ValueCountFrequency (%)
1561
 
< 0.1%
15312
 
< 0.1%
1526
 
< 0.1%
145778
 
0.3%
1209658
 
4.1%
86109534
47.0%
672405
 
1.0%
5127204
 
11.7%
4910220
 
4.4%
4816710
 
7.2%

Current_pincode_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6698
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3396.880247
Minimum1
Maximum7345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:26.956984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile263
Q11511
median2970
Q35677
95-th percentile6942
Maximum7345
Range7344
Interquartile range (IQR)4166

Descriptive statistics

Standard deviation2238.147502
Coefficient of variation (CV)0.6588832515
Kurtosis-1.286741268
Mean3396.880247
Median Absolute Deviation (MAD)1915
Skewness0.2781846819
Sum791996217
Variance5009304.239
MonotonicityNot monotonic
2022-11-01T17:44:27.188055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25781880
 
0.8%
14461731
 
0.7%
15151087
 
0.5%
2989934
 
0.4%
2943899
 
0.4%
1509864
 
0.4%
2782841
 
0.4%
1794809
 
0.3%
571781
 
0.3%
3363730
 
0.3%
Other values (6688)222598
95.5%
ValueCountFrequency (%)
126
 
< 0.1%
272
 
< 0.1%
350
 
< 0.1%
488
< 0.1%
5215
0.1%
6100
< 0.1%
7104
< 0.1%
843
 
< 0.1%
929
 
< 0.1%
105
 
< 0.1%
ValueCountFrequency (%)
73457
 
< 0.1%
73441
 
< 0.1%
73432
 
< 0.1%
73421
 
< 0.1%
73417
 
< 0.1%
73402
 
< 0.1%
73382
 
< 0.1%
73373
 
< 0.1%
733620
< 0.1%
73351
 
< 0.1%

Date.of.Birth
Categorical

HIGH CARDINALITY

Distinct15433
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
01-01-88
 
2173
01-01-90
 
2170
01-01-87
 
2127
01-01-86
 
2063
01-01-85
 
2005
Other values (15428)
222616 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1865232
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1384 ?
Unique (%)0.6%

Sample

1st row01-01-84
2nd row31-07-85
3rd row24-08-85
4th row30-12-93
5th row09-12-77

Common Values

ValueCountFrequency (%)
01-01-882173
 
0.9%
01-01-902170
 
0.9%
01-01-872127
 
0.9%
01-01-862063
 
0.9%
01-01-852005
 
0.9%
01-01-911985
 
0.9%
01-01-891962
 
0.8%
01-01-931930
 
0.8%
01-01-951924
 
0.8%
01-01-921924
 
0.8%
Other values (15423)212891
91.3%

Length

2022-11-01T17:44:27.398861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01-01-882173
 
0.9%
01-01-902170
 
0.9%
01-01-872127
 
0.9%
01-01-862063
 
0.9%
01-01-852005
 
0.9%
01-01-911985
 
0.9%
01-01-891962
 
0.8%
01-01-931930
 
0.8%
01-01-951924
 
0.8%
01-01-921924
 
0.8%
Other values (15423)212891
91.3%

Most occurring characters

ValueCountFrequency (%)
-466308
25.0%
0380641
20.4%
1282238
15.1%
8126724
 
6.8%
9124952
 
6.7%
2111802
 
6.0%
7103820
 
5.6%
685415
 
4.6%
570721
 
3.8%
358819
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1398924
75.0%
Dash Punctuation466308
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0380641
27.2%
1282238
20.2%
8126724
 
9.1%
9124952
 
8.9%
2111802
 
8.0%
7103820
 
7.4%
685415
 
6.1%
570721
 
5.1%
358819
 
4.2%
453792
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
-466308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1865232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-466308
25.0%
0380641
20.4%
1282238
15.1%
8126724
 
6.8%
9124952
 
6.7%
2111802
 
6.0%
7103820
 
5.6%
685415
 
4.6%
570721
 
3.8%
358819
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1865232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-466308
25.0%
0380641
20.4%
1282238
15.1%
8126724
 
6.8%
9124952
 
6.7%
2111802
 
6.0%
7103820
 
5.6%
685415
 
4.6%
570721
 
3.8%
358819
 
3.2%

Employment.Type
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing7661
Missing (%)3.3%
Memory size1.8 MiB
Self employed
127635 
Salaried
97858 

Length

Max length13
Median length13
Mean length10.8301322
Min length8

Characters and Unicode

Total characters2442119
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalaried
2nd rowSelf employed
3rd rowSelf employed
4th rowSelf employed
5th rowSelf employed

Common Values

ValueCountFrequency (%)
Self employed127635
54.7%
Salaried97858
42.0%
(Missing)7661
 
3.3%

Length

2022-11-01T17:44:27.569816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:27.779821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
self127635
36.1%
employed127635
36.1%
salaried97858
27.7%

Most occurring characters

ValueCountFrequency (%)
e480763
19.7%
l353128
14.5%
S225493
9.2%
d225493
9.2%
a195716
8.0%
f127635
 
5.2%
127635
 
5.2%
m127635
 
5.2%
p127635
 
5.2%
o127635
 
5.2%
Other values (3)323351
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2088991
85.5%
Uppercase Letter225493
 
9.2%
Space Separator127635
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e480763
23.0%
l353128
16.9%
d225493
10.8%
a195716
9.4%
f127635
 
6.1%
m127635
 
6.1%
p127635
 
6.1%
o127635
 
6.1%
y127635
 
6.1%
r97858
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
S225493
100.0%
Space Separator
ValueCountFrequency (%)
127635
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2314484
94.8%
Common127635
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e480763
20.8%
l353128
15.3%
S225493
9.7%
d225493
9.7%
a195716
8.5%
f127635
 
5.5%
m127635
 
5.5%
p127635
 
5.5%
o127635
 
5.5%
y127635
 
5.5%
Other values (2)195716
8.5%
Common
ValueCountFrequency (%)
127635
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2442119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e480763
19.7%
l353128
14.5%
S225493
9.2%
d225493
9.2%
a195716
8.0%
f127635
 
5.2%
127635
 
5.2%
m127635
 
5.2%
p127635
 
5.2%
o127635
 
5.2%
Other values (3)323351
13.2%

DisbursalDate
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
31-10-18
 
8826
24-10-18
 
6701
31-08-18
 
6690
23-10-18
 
6440
26-10-18
 
6215
Other values (79)
198282 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1865232
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row03-08-18
2nd row26-09-18
3rd row01-08-18
4th row26-10-18
5th row26-09-18

Common Values

ValueCountFrequency (%)
31-10-188826
 
3.8%
24-10-186701
 
2.9%
31-08-186690
 
2.9%
23-10-186440
 
2.8%
26-10-186215
 
2.7%
25-10-185944
 
2.5%
22-10-185928
 
2.5%
30-10-185837
 
2.5%
30-08-184664
 
2.0%
29-10-184389
 
1.9%
Other values (74)171520
73.6%

Length

2022-11-01T17:44:27.926854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31-10-188826
 
3.8%
24-10-186701
 
2.9%
31-08-186690
 
2.9%
23-10-186440
 
2.8%
26-10-186215
 
2.7%
25-10-185944
 
2.5%
22-10-185928
 
2.5%
30-10-185837
 
2.5%
30-08-184664
 
2.0%
29-10-184389
 
1.9%
Other values (74)171520
73.6%

Most occurring characters

ValueCountFrequency (%)
-466308
25.0%
1428718
23.0%
8323341
17.3%
0297295
15.9%
2121474
 
6.5%
988434
 
4.7%
351363
 
2.8%
424447
 
1.3%
623674
 
1.3%
720740
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1398924
75.0%
Dash Punctuation466308
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1428718
30.6%
8323341
23.1%
0297295
21.3%
2121474
 
8.7%
988434
 
6.3%
351363
 
3.7%
424447
 
1.7%
623674
 
1.7%
720740
 
1.5%
519438
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-466308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1865232
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-466308
25.0%
1428718
23.0%
8323341
17.3%
0297295
15.9%
2121474
 
6.5%
988434
 
4.7%
351363
 
2.8%
424447
 
1.3%
623674
 
1.3%
720740
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1865232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-466308
25.0%
1428718
23.0%
8323341
17.3%
0297295
15.9%
2121474
 
6.5%
988434
 
4.7%
351363
 
2.8%
424447
 
1.3%
623674
 
1.3%
720740
 
1.1%

State_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.262242981
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:28.091760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q310
95-th percentile16
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.482229504
Coefficient of variation (CV)0.6171963009
Kurtosis-0.3282549048
Mean7.262242981
Median Absolute Deviation (MAD)3
Skewness0.8219713926
Sum1693221
Variance20.09038133
MonotonicityNot monotonic
2022-11-01T17:44:28.256135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
444870
19.2%
334078
14.6%
633505
14.4%
1317884
 
7.7%
916022
 
6.9%
814197
 
6.1%
510177
 
4.4%
149414
 
4.0%
18936
 
3.8%
76786
 
2.9%
Other values (12)37285
16.0%
ValueCountFrequency (%)
18936
 
3.8%
24160
 
1.8%
334078
14.6%
444870
19.2%
510177
 
4.4%
633505
14.4%
76786
 
2.9%
814197
 
6.1%
916022
 
6.9%
103605
 
1.5%
ValueCountFrequency (%)
2276
 
< 0.1%
21156
 
0.1%
20185
 
0.1%
191035
 
0.4%
185412
 
2.3%
173991
 
1.7%
162685
 
1.2%
155049
 
2.2%
149414
4.0%
1317884
7.7%

Employee_code_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3270
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1549.477148
Minimum1
Maximum3795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:28.453341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile149
Q1713
median1451
Q32362
95-th percentile3186
Maximum3795
Range3794
Interquartile range (IQR)1649

Descriptive statistics

Standard deviation975.2612778
Coefficient of variation (CV)0.6294131404
Kurtosis-1.052600731
Mean1549.477148
Median Absolute Deviation (MAD)810
Skewness0.2443133823
Sum361266795
Variance951134.5599
MonotonicityNot monotonic
2022-11-01T17:44:28.674412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2546628
 
0.3%
620502
 
0.2%
255494
 
0.2%
130408
 
0.2%
2153401
 
0.2%
956369
 
0.2%
184361
 
0.2%
1466355
 
0.2%
1494352
 
0.2%
64351
 
0.2%
Other values (3260)228933
98.2%
ValueCountFrequency (%)
180
< 0.1%
3149
0.1%
467
< 0.1%
599
< 0.1%
7144
0.1%
956
 
< 0.1%
1044
 
< 0.1%
1185
< 0.1%
12119
0.1%
1588
< 0.1%
ValueCountFrequency (%)
37951
 
< 0.1%
37941
 
< 0.1%
37931
 
< 0.1%
37921
 
< 0.1%
37913
< 0.1%
37901
 
< 0.1%
37892
< 0.1%
37881
 
< 0.1%
37872
< 0.1%
37863
< 0.1%

MobileNo_Avl_Flag
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
1
233154 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233154
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1233154
100.0%

Length

2022-11-01T17:44:28.884308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:29.035799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1233154
100.0%

Most occurring characters

ValueCountFrequency (%)
1233154
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number233154
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1233154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common233154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1233154
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII233154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1233154
100.0%

Aadhar_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
1
195924 
0
37230 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233154
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1195924
84.0%
037230
 
16.0%

Length

2022-11-01T17:44:29.166569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:29.323064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1195924
84.0%
037230
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1195924
84.0%
037230
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number233154
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1195924
84.0%
037230
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common233154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1195924
84.0%
037230
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII233154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1195924
84.0%
037230
 
16.0%

PAN_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
215533 
1
 
17621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233154
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0215533
92.4%
117621
 
7.6%

Length

2022-11-01T17:44:29.467593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:29.618387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0215533
92.4%
117621
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0215533
92.4%
117621
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number233154
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0215533
92.4%
117621
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common233154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0215533
92.4%
117621
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII233154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0215533
92.4%
117621
 
7.6%

VoterID_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
199360 
1
33794 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233154
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0199360
85.5%
133794
 
14.5%

Length

2022-11-01T17:44:29.757108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:29.919757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0199360
85.5%
133794
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0199360
85.5%
133794
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number233154
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0199360
85.5%
133794
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common233154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0199360
85.5%
133794
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII233154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0199360
85.5%
133794
 
14.5%

Driving_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
227735 
1
 
5419

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233154
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0227735
97.7%
15419
 
2.3%

Length

2022-11-01T17:44:30.058470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:30.211280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0227735
97.7%
15419
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0227735
97.7%
15419
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number233154
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0227735
97.7%
15419
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common233154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0227735
97.7%
15419
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII233154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0227735
97.7%
15419
 
2.3%

Passport_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
232658 
1
 
496

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233154
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0232658
99.8%
1496
 
0.2%

Length

2022-11-01T17:44:30.351735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:30.502541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0232658
99.8%
1496
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0232658
99.8%
1496
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number233154
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0232658
99.8%
1496
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common233154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0232658
99.8%
1496
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII233154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0232658
99.8%
1496
 
0.2%

PERFORM_CNS.SCORE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct573
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.4629944
Minimum0
Maximum890
Zeros116950
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:30.673403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3678
95-th percentile825
Maximum890
Range890
Interquartile range (IQR)678

Descriptive statistics

Standard deviation338.374779
Coefficient of variation (CV)1.168974223
Kurtosis-1.635257516
Mean289.4629944
Median Absolute Deviation (MAD)0
Skewness0.4451504642
Sum67489455
Variance114497.4911
MonotonicityNot monotonic
2022-11-01T17:44:30.893296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0116950
50.2%
3008776
 
3.8%
7388662
 
3.7%
8257393
 
3.2%
153765
 
1.6%
173672
 
1.6%
7633026
 
1.3%
162885
 
1.2%
7082104
 
0.9%
7371989
 
0.9%
Other values (563)73932
31.7%
ValueCountFrequency (%)
0116950
50.2%
113
 
< 0.1%
14976
 
0.4%
153765
 
1.6%
162885
 
1.2%
173672
 
1.6%
181534
 
0.7%
3008776
 
3.8%
3019
 
< 0.1%
30217
 
< 0.1%
ValueCountFrequency (%)
8904
 
< 0.1%
8841
 
< 0.1%
87962
< 0.1%
8787
 
< 0.1%
8739
 
< 0.1%
87028
< 0.1%
8697
 
< 0.1%
8682
 
< 0.1%
8671
 
< 0.1%
8649
 
< 0.1%

PERFORM_CNS.SCORE.DESCRIPTION
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
No Bureau History Available
116950 
C-Very Low Risk
16045 
A-Very Low Risk
14124 
D-Very Low Risk
 
11358
B-Very Low Risk
 
9201
Other values (15)
65476 

Length

Max length55
Median length27
Mean length22.20342778
Min length10

Characters and Unicode

Total characters5176818
Distinct characters50
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Bureau History Available
2nd rowI-Medium Risk
3rd rowNo Bureau History Available
4th rowL-Very High Risk
5th rowNo Bureau History Available

Common Values

ValueCountFrequency (%)
No Bureau History Available116950
50.2%
C-Very Low Risk16045
 
6.9%
A-Very Low Risk14124
 
6.1%
D-Very Low Risk11358
 
4.9%
B-Very Low Risk9201
 
3.9%
M-Very High Risk8776
 
3.8%
F-Low Risk8485
 
3.6%
K-High Risk8277
 
3.6%
H-Medium Risk6855
 
2.9%
E-Low Risk5821
 
2.5%
Other values (10)27262
 
11.7%

Length

2022-11-01T17:44:31.193043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
available125921
15.1%
no121369
14.5%
history120715
14.4%
bureau116950
14.0%
risk103369
12.4%
low50728
 
6.1%
not20272
 
2.4%
c-very16045
 
1.9%
a-very14124
 
1.7%
scored12835
 
1.5%
Other values (35)133161
15.9%

Most occurring characters

ValueCountFrequency (%)
602335
 
11.6%
i402074
 
7.8%
a382885
 
7.4%
o367190
 
7.1%
e355830
 
6.9%
r319650
 
6.2%
u261288
 
5.0%
l254352
 
4.9%
s238131
 
4.6%
y185214
 
3.6%
Other values (40)1807869
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3545463
68.5%
Uppercase Letter903972
 
17.5%
Space Separator602335
 
11.6%
Dash Punctuation103369
 
2.0%
Other Punctuation12835
 
0.2%
Decimal Number3074
 
0.1%
Open Punctuation2885
 
0.1%
Close Punctuation2885
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i402074
11.3%
a382885
10.8%
o367190
10.4%
e355830
10.0%
r319650
9.0%
u261288
7.4%
l254352
7.2%
s238131
 
6.7%
y185214
 
5.2%
t172108
 
4.9%
Other values (12)606741
17.1%
Uppercase Letter
ValueCountFrequency (%)
H149505
16.5%
N141641
15.7%
A137727
15.2%
B126151
14.0%
R103369
11.4%
L70156
7.8%
V60638
6.7%
M21191
 
2.3%
S16600
 
1.8%
C16045
 
1.8%
Other values (9)60949
6.7%
Decimal Number
ValueCountFrequency (%)
31534
49.9%
61534
49.9%
53
 
0.1%
03
 
0.1%
Space Separator
ValueCountFrequency (%)
602335
100.0%
Dash Punctuation
ValueCountFrequency (%)
-103369
100.0%
Other Punctuation
ValueCountFrequency (%)
:12835
100.0%
Open Punctuation
ValueCountFrequency (%)
(2885
100.0%
Close Punctuation
ValueCountFrequency (%)
)2885
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4449435
85.9%
Common727383
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i402074
 
9.0%
a382885
 
8.6%
o367190
 
8.3%
e355830
 
8.0%
r319650
 
7.2%
u261288
 
5.9%
l254352
 
5.7%
s238131
 
5.4%
y185214
 
4.2%
t172108
 
3.9%
Other values (31)1510713
34.0%
Common
ValueCountFrequency (%)
602335
82.8%
-103369
 
14.2%
:12835
 
1.8%
(2885
 
0.4%
)2885
 
0.4%
31534
 
0.2%
61534
 
0.2%
53
 
< 0.1%
03
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5176818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
602335
 
11.6%
i402074
 
7.8%
a382885
 
7.4%
o367190
 
7.1%
e355830
 
6.9%
r319650
 
6.2%
u261288
 
5.0%
l254352
 
4.9%
s238131
 
4.6%
y185214
 
3.6%
Other values (40)1807869
34.9%

PRI.NO.OF.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct108
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.440635803
Minimum0
Maximum453
Zeros116950
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:31.456088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile11
Maximum453
Range453
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.217233022
Coefficient of variation (CV)2.137653236
Kurtosis415.6865802
Mean2.440635803
Median Absolute Deviation (MAD)0
Skewness9.744845323
Sum569044
Variance27.2195204
MonotonicityNot monotonic
2022-11-01T17:44:31.725632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0116950
50.2%
134978
 
15.0%
219784
 
8.5%
313015
 
5.6%
49323
 
4.0%
57222
 
3.1%
65557
 
2.4%
74411
 
1.9%
83570
 
1.5%
92884
 
1.2%
Other values (98)15460
 
6.6%
ValueCountFrequency (%)
0116950
50.2%
134978
 
15.0%
219784
 
8.5%
313015
 
5.6%
49323
 
4.0%
57222
 
3.1%
65557
 
2.4%
74411
 
1.9%
83570
 
1.5%
92884
 
1.2%
ValueCountFrequency (%)
4531
< 0.1%
3541
< 0.1%
2711
< 0.1%
1941
< 0.1%
1482
< 0.1%
1471
< 0.1%
1361
< 0.1%
1321
< 0.1%
1311
< 0.1%
1241
< 0.1%

PRI.ACTIVE.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.039896378
Minimum0
Maximum144
Zeros137016
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:31.995169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum144
Range144
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.941496441
Coefficient of variation (CV)1.867009524
Kurtosis155.1340347
Mean1.039896378
Median Absolute Deviation (MAD)0
Skewness5.371850177
Sum242456
Variance3.769408431
MonotonicityNot monotonic
2022-11-01T17:44:32.261600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0137016
58.8%
142055
 
18.0%
221549
 
9.2%
312268
 
5.3%
47460
 
3.2%
54542
 
1.9%
62788
 
1.2%
71795
 
0.8%
81202
 
0.5%
9756
 
0.3%
Other values (30)1723
 
0.7%
ValueCountFrequency (%)
0137016
58.8%
142055
 
18.0%
221549
 
9.2%
312268
 
5.3%
47460
 
3.2%
54542
 
1.9%
62788
 
1.2%
71795
 
0.8%
81202
 
0.5%
9756
 
0.3%
ValueCountFrequency (%)
1441
< 0.1%
651
< 0.1%
521
< 0.1%
431
< 0.1%
421
< 0.1%
391
< 0.1%
372
< 0.1%
352
< 0.1%
342
< 0.1%
322
< 0.1%

PRI.OVERDUE.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1565488904
Minimum0
Maximum25
Zeros206879
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:32.468617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5487867499
Coefficient of variation (CV)3.505529476
Kurtosis125.7317653
Mean0.1565488904
Median Absolute Deviation (MAD)0
Skewness7.512927466
Sum36500
Variance0.3011668968
MonotonicityNot monotonic
2022-11-01T17:44:32.649542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0206879
88.7%
119970
 
8.6%
24302
 
1.8%
31202
 
0.5%
4404
 
0.2%
5166
 
0.1%
696
 
< 0.1%
738
 
< 0.1%
827
 
< 0.1%
925
 
< 0.1%
Other values (12)45
 
< 0.1%
ValueCountFrequency (%)
0206879
88.7%
119970
 
8.6%
24302
 
1.8%
31202
 
0.5%
4404
 
0.2%
5166
 
0.1%
696
 
< 0.1%
738
 
< 0.1%
827
 
< 0.1%
925
 
< 0.1%
ValueCountFrequency (%)
251
 
< 0.1%
231
 
< 0.1%
191
 
< 0.1%
182
 
< 0.1%
172
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
145
< 0.1%
135
< 0.1%
128
< 0.1%

PRI.CURRENT.BALANCE
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct71341
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165900.0769
Minimum-6678296
Maximum96524920
Zeros141696
Zeros (%)60.8%
Negative448
Negative (%)0.2%
Memory size1.8 MiB
2022-11-01T17:44:32.880030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-6678296
5-th percentile0
Q10
median0
Q335006.5
95-th percentile804263.8
Maximum96524920
Range103203216
Interquartile range (IQR)35006.5

Descriptive statistics

Standard deviation942273.5824
Coefficient of variation (CV)5.679765795
Kurtosis1616.817918
Mean165900.0769
Median Absolute Deviation (MAD)0
Skewness29.42581325
Sum3.868026654 × 1010
Variance8.87879504 × 1011
MonotonicityNot monotonic
2022-11-01T17:44:33.165208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0141696
60.8%
800121
 
0.1%
400119
 
0.1%
30000100
 
< 0.1%
5000084
 
< 0.1%
10000083
 
< 0.1%
4000077
 
< 0.1%
2500073
 
< 0.1%
2000066
 
< 0.1%
6000061
 
< 0.1%
Other values (71331)90674
38.9%
ValueCountFrequency (%)
-66782961
< 0.1%
-20183091
< 0.1%
-17384151
< 0.1%
-14083141
< 0.1%
-13064491
< 0.1%
-11782421
< 0.1%
-11081141
< 0.1%
-9316441
< 0.1%
-7635991
< 0.1%
-7540601
< 0.1%
ValueCountFrequency (%)
965249201
< 0.1%
756034001
< 0.1%
664061601
< 0.1%
635313201
< 0.1%
633590401
< 0.1%
613676881
< 0.1%
563858241
< 0.1%
561635441
< 0.1%
525031521
< 0.1%
523679601
< 0.1%

PRI.SANCTIONED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct44390
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218503.8553
Minimum0
Maximum1000000000
Zeros138096
Zeros (%)59.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:33.402558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q362500
95-th percentile1030280.05
Maximum1000000000
Range1000000000
Interquartile range (IQR)62500

Descriptive statistics

Standard deviation2374794.126
Coefficient of variation (CV)10.86843123
Kurtosis134834.0411
Mean218503.8553
Median Absolute Deviation (MAD)0
Skewness323.6972121
Sum5.094504788 × 1010
Variance5.63964714 × 1012
MonotonicityNot monotonic
2022-11-01T17:44:33.634760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0138096
59.2%
500001503
 
0.6%
300001450
 
0.6%
100000974
 
0.4%
25000946
 
0.4%
40000873
 
0.4%
20000858
 
0.4%
60000611
 
0.3%
200000607
 
0.3%
15000565
 
0.2%
Other values (44380)86671
37.2%
ValueCountFrequency (%)
0138096
59.2%
135
 
< 0.1%
224
 
< 0.1%
320
 
< 0.1%
420
 
< 0.1%
515
 
< 0.1%
610
 
< 0.1%
713
 
< 0.1%
815
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
10000000001
< 0.1%
1058657121
< 0.1%
1004250001
< 0.1%
926228161
< 0.1%
863238881
< 0.1%
803275601
< 0.1%
790127521
< 0.1%
761287121
< 0.1%
698474561
< 0.1%
698280001
< 0.1%

PRI.DISBURSED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct47909
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218065.8987
Minimum0
Maximum1000000000
Zeros138204
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:33.863612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q360800
95-th percentile1027992.85
Maximum1000000000
Range1000000000
Interquartile range (IQR)60800

Descriptive statistics

Standard deviation2377743.846
Coefficient of variation (CV)10.90378579
Kurtosis134167.2476
Mean218065.8987
Median Absolute Deviation (MAD)0
Skewness322.5414945
Sum5.084293654 × 1010
Variance5.653665798 × 1012
MonotonicityNot monotonic
2022-11-01T17:44:34.106508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0138204
59.3%
500001398
 
0.6%
300001344
 
0.6%
100000949
 
0.4%
40000794
 
0.3%
25000748
 
0.3%
20000670
 
0.3%
200000613
 
0.3%
300000549
 
0.2%
60000542
 
0.2%
Other values (47899)87343
37.5%
ValueCountFrequency (%)
0138204
59.3%
144
 
< 0.1%
225
 
< 0.1%
320
 
< 0.1%
419
 
< 0.1%
515
 
< 0.1%
610
 
< 0.1%
713
 
< 0.1%
815
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
10000000001
< 0.1%
1057557121
< 0.1%
1004250001
< 0.1%
926287281
< 0.1%
860247841
< 0.1%
803491681
< 0.1%
790127521
< 0.1%
761287121
< 0.1%
698474561
< 0.1%
697159441
< 0.1%

SEC.NO.OF.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05908112235
Minimum0
Maximum52
Zeros227289
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:34.317730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum52
Range52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6267945848
Coefficient of variation (CV)10.60905006
Kurtosis1283.023577
Mean0.05908112235
Median Absolute Deviation (MAD)0
Skewness27.98609032
Sum13775
Variance0.3928714515
MonotonicityNot monotonic
2022-11-01T17:44:34.526678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0227289
97.5%
13466
 
1.5%
21036
 
0.4%
3444
 
0.2%
4292
 
0.1%
5148
 
0.1%
6119
 
0.1%
775
 
< 0.1%
868
 
< 0.1%
938
 
< 0.1%
Other values (27)179
 
0.1%
ValueCountFrequency (%)
0227289
97.5%
13466
 
1.5%
21036
 
0.4%
3444
 
0.2%
4292
 
0.1%
5148
 
0.1%
6119
 
0.1%
775
 
< 0.1%
868
 
< 0.1%
938
 
< 0.1%
ValueCountFrequency (%)
521
 
< 0.1%
462
< 0.1%
421
 
< 0.1%
382
< 0.1%
371
 
< 0.1%
351
 
< 0.1%
342
< 0.1%
314
< 0.1%
302
< 0.1%
291
 
< 0.1%

SEC.ACTIVE.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02770272009
Minimum0
Maximum36
Zeros229337
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:34.710364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3160566085
Coefficient of variation (CV)11.40886554
Kurtosis1771.260421
Mean0.02770272009
Median Absolute Deviation (MAD)0
Skewness30.59951015
Sum6459
Variance0.09989177978
MonotonicityNot monotonic
2022-11-01T17:44:34.891070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0229337
98.4%
12684
 
1.2%
2636
 
0.3%
3195
 
0.1%
4116
 
< 0.1%
565
 
< 0.1%
632
 
< 0.1%
722
 
< 0.1%
817
 
< 0.1%
911
 
< 0.1%
Other values (13)39
 
< 0.1%
ValueCountFrequency (%)
0229337
98.4%
12684
 
1.2%
2636
 
0.3%
3195
 
0.1%
4116
 
< 0.1%
565
 
< 0.1%
632
 
< 0.1%
722
 
< 0.1%
817
 
< 0.1%
911
 
< 0.1%
ValueCountFrequency (%)
361
 
< 0.1%
261
 
< 0.1%
222
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
171
 
< 0.1%
162
< 0.1%
154
< 0.1%
141
 
< 0.1%
133
< 0.1%

SEC.OVERDUE.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007244139067
Minimum0
Maximum8
Zeros231817
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:35.062111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1110789236
Coefficient of variation (CV)15.33362662
Kurtosis866.5712603
Mean0.007244139067
Median Absolute Deviation (MAD)0
Skewness24.12927125
Sum1689
Variance0.01233852727
MonotonicityNot monotonic
2022-11-01T17:44:35.223090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0231817
99.4%
11129
 
0.5%
2126
 
0.1%
347
 
< 0.1%
419
 
< 0.1%
58
 
< 0.1%
66
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
0231817
99.4%
11129
 
0.5%
2126
 
0.1%
347
 
< 0.1%
419
 
< 0.1%
58
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
71
 
< 0.1%
66
 
< 0.1%
58
 
< 0.1%
419
 
< 0.1%
347
 
< 0.1%
2126
 
0.1%
11129
 
0.5%
0231817
99.4%

SEC.CURRENT.BALANCE
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3246
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5427.792819
Minimum-574647
Maximum36032852
Zeros229790
Zeros (%)98.6%
Negative61
Negative (%)< 0.1%
Memory size1.8 MiB
2022-11-01T17:44:35.435158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-574647
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36032852
Range36607499
Interquartile range (IQR)0

Descriptive statistics

Standard deviation170236.9946
Coefficient of variation (CV)31.36394485
Kurtosis17243.66322
Mean5427.792819
Median Absolute Deviation (MAD)0
Skewness108.5062952
Sum1265511607
Variance2.898063434 × 1010
MonotonicityNot monotonic
2022-11-01T17:44:35.662857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0229790
98.6%
80010
 
< 0.1%
4008
 
< 0.1%
1008
 
< 0.1%
12006
 
< 0.1%
5896
 
< 0.1%
-15
 
< 0.1%
16004
 
< 0.1%
14
 
< 0.1%
10704
 
< 0.1%
Other values (3236)3309
 
1.4%
ValueCountFrequency (%)
-5746471
< 0.1%
-2397821
< 0.1%
-1555271
< 0.1%
-1171381
< 0.1%
-312901
< 0.1%
-200001
< 0.1%
-96251
< 0.1%
-86061
< 0.1%
-77301
< 0.1%
-73701
< 0.1%
ValueCountFrequency (%)
360328521
< 0.1%
295605401
< 0.1%
246920241
< 0.1%
224971721
< 0.1%
196382801
< 0.1%
136078821
< 0.1%
120801021
< 0.1%
107792611
< 0.1%
107160391
< 0.1%
98011341
< 0.1%

SEC.SANCTIONED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2223
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7295.923347
Minimum0
Maximum30000000
Zeros229418
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:35.905957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30000000
Range30000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation183155.9931
Coefficient of variation (CV)25.10388122
Kurtosis8673.756765
Mean7295.923347
Median Absolute Deviation (MAD)0
Skewness75.25493196
Sum1701073712
Variance3.35461178 × 1010
MonotonicityNot monotonic
2022-11-01T17:44:36.145564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0229418
98.4%
5000083
 
< 0.1%
10000061
 
< 0.1%
3000044
 
< 0.1%
4000039
 
< 0.1%
20000038
 
< 0.1%
1500036
 
< 0.1%
2500035
 
< 0.1%
1000033
 
< 0.1%
30000031
 
< 0.1%
Other values (2213)3336
 
1.4%
ValueCountFrequency (%)
0229418
98.4%
16
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
181
 
< 0.1%
191
 
< 0.1%
231
 
< 0.1%
301
 
< 0.1%
321
 
< 0.1%
521
 
< 0.1%
ValueCountFrequency (%)
300000001
< 0.1%
268882001
< 0.1%
250000001
< 0.1%
198000001
< 0.1%
186910021
< 0.1%
136078821
< 0.1%
126260001
< 0.1%
125119901
< 0.1%
120143001
< 0.1%
119000001
< 0.1%

SEC.DISBURSED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2553
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7179.997873
Minimum0
Maximum30000000
Zeros229450
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:36.393530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30000000
Range30000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation182592.5001
Coefficient of variation (CV)25.43071785
Kurtosis8773.794348
Mean7179.997873
Median Absolute Deviation (MAD)0
Skewness75.76425191
Sum1674045224
Variance3.334002108 × 1010
MonotonicityNot monotonic
2022-11-01T17:44:36.609703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0229450
98.4%
5000059
 
< 0.1%
10000047
 
< 0.1%
20000036
 
< 0.1%
4000031
 
< 0.1%
30000030
 
< 0.1%
3000026
 
< 0.1%
50000025
 
< 0.1%
15000023
 
< 0.1%
40000021
 
< 0.1%
Other values (2543)3406
 
1.5%
ValueCountFrequency (%)
0229450
98.4%
15
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
181
 
< 0.1%
191
 
< 0.1%
231
 
< 0.1%
301
 
< 0.1%
321
 
< 0.1%
521
 
< 0.1%
ValueCountFrequency (%)
300000001
< 0.1%
268882001
< 0.1%
250000001
< 0.1%
198000001
< 0.1%
186910021
< 0.1%
136078821
< 0.1%
126260001
< 0.1%
125119901
< 0.1%
120143001
< 0.1%
119000001
< 0.1%

PRIMARY.INSTAL.AMT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct28067
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13105.48172
Minimum0
Maximum25642806
Zeros159517
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:36.852181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31999
95-th percentile26376.45
Maximum25642806
Range25642806
Interquartile range (IQR)1999

Descriptive statistics

Standard deviation151367.9047
Coefficient of variation (CV)11.54996878
Kurtosis8165.596601
Mean13105.48172
Median Absolute Deviation (MAD)0
Skewness69.91615647
Sum3055595485
Variance2.291224258 × 1010
MonotonicityNot monotonic
2022-11-01T17:44:37.081582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0159517
68.4%
1620292
 
0.1%
1500156
 
0.1%
1600144
 
0.1%
2000141
 
0.1%
2500136
 
0.1%
1149128
 
0.1%
1250124
 
0.1%
1700111
 
< 0.1%
1350103
 
< 0.1%
Other values (28057)72302
31.0%
ValueCountFrequency (%)
0159517
68.4%
15
 
< 0.1%
24
 
< 0.1%
319
 
< 0.1%
415
 
< 0.1%
512
 
< 0.1%
622
 
< 0.1%
714
 
< 0.1%
814
 
< 0.1%
918
 
< 0.1%
ValueCountFrequency (%)
256428061
< 0.1%
207665531
< 0.1%
174088221
< 0.1%
155185461
< 0.1%
154204111
< 0.1%
150199141
< 0.1%
145992521
< 0.1%
128701911
< 0.1%
113055791
< 0.1%
84700591
< 0.1%

SEC.INSTAL.AMT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1918
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.2684492
Minimum0
Maximum4170901
Zeros230937
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:37.328791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4170901
Range4170901
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15553.69134
Coefficient of variation (CV)48.11385515
Kurtosis33069.81458
Mean323.2684492
Median Absolute Deviation (MAD)0
Skewness153.8063689
Sum75371332
Variance241917314.3
MonotonicityNot monotonic
2022-11-01T17:44:37.554465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0230937
99.0%
21007
 
< 0.1%
12326
 
< 0.1%
11006
 
< 0.1%
10656
 
< 0.1%
50006
 
< 0.1%
15655
 
< 0.1%
18345
 
< 0.1%
24005
 
< 0.1%
500005
 
< 0.1%
Other values (1908)2166
 
0.9%
ValueCountFrequency (%)
0230937
99.0%
14
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
122
 
< 0.1%
162
 
< 0.1%
ValueCountFrequency (%)
41709011
< 0.1%
32467101
< 0.1%
18140001
< 0.1%
16612201
< 0.1%
15899461
< 0.1%
14476001
< 0.1%
12311661
< 0.1%
11131181
< 0.1%
10200001
< 0.1%
8424831
< 0.1%

NEW.ACCTS.IN.LAST.SIX.MONTHS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3818334663
Minimum0
Maximum35
Zeros181494
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:37.764218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9551067235
Coefficient of variation (CV)2.501369858
Kurtosis47.34398878
Mean0.3818334663
Median Absolute Deviation (MAD)0
Skewness4.83932582
Sum89026
Variance0.9122288533
MonotonicityNot monotonic
2022-11-01T17:44:37.942617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0181494
77.8%
132099
 
13.8%
211015
 
4.7%
34458
 
1.9%
41957
 
0.8%
5964
 
0.4%
6480
 
0.2%
7302
 
0.1%
8147
 
0.1%
979
 
< 0.1%
Other values (16)159
 
0.1%
ValueCountFrequency (%)
0181494
77.8%
132099
 
13.8%
211015
 
4.7%
34458
 
1.9%
41957
 
0.8%
5964
 
0.4%
6480
 
0.2%
7302
 
0.1%
8147
 
0.1%
979
 
< 0.1%
ValueCountFrequency (%)
351
 
< 0.1%
281
 
< 0.1%
232
 
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
203
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
176
< 0.1%
166
< 0.1%

DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09748063512
Minimum0
Maximum20
Zeros214959
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:38.137714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3844389912
Coefficient of variation (CV)3.943747297
Kurtosis98.77513272
Mean0.09748063512
Median Absolute Deviation (MAD)0
Skewness6.641995783
Sum22728
Variance0.147793338
MonotonicityNot monotonic
2022-11-01T17:44:38.303257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0214959
92.2%
114941
 
6.4%
22470
 
1.1%
3537
 
0.2%
4138
 
0.1%
558
 
< 0.1%
620
 
< 0.1%
713
 
< 0.1%
87
 
< 0.1%
123
 
< 0.1%
Other values (4)8
 
< 0.1%
ValueCountFrequency (%)
0214959
92.2%
114941
 
6.4%
22470
 
1.1%
3537
 
0.2%
4138
 
0.1%
558
 
< 0.1%
620
 
< 0.1%
713
 
< 0.1%
87
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
123
 
< 0.1%
113
 
< 0.1%
102
 
< 0.1%
92
 
< 0.1%
87
 
< 0.1%
713
 
< 0.1%
620
 
< 0.1%
558
< 0.1%
4138
0.1%

AVERAGE.ACCT.AGE
Categorical

HIGH CARDINALITY

Distinct192
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0yrs 0mon
119373 
0yrs 6mon
 
6028
0yrs 7mon
 
5366
0yrs 11mon
 
5237
0yrs 10mon
 
5143
Other values (187)
92007 

Length

Max length11
Median length9
Mean length9.077193615
Min length9

Characters and Unicode

Total characters2116384
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st row0yrs 0mon
2nd row1yrs 11mon
3rd row0yrs 0mon
4th row0yrs 8mon
5th row0yrs 0mon

Common Values

ValueCountFrequency (%)
0yrs 0mon119373
51.2%
0yrs 6mon6028
 
2.6%
0yrs 7mon5366
 
2.3%
0yrs 11mon5237
 
2.2%
0yrs 10mon5143
 
2.2%
1yrs 0mon5031
 
2.2%
0yrs 9mon5018
 
2.2%
0yrs 8mon4892
 
2.1%
1yrs 1mon4465
 
1.9%
0yrs 5mon4354
 
1.9%
Other values (182)68247
29.3%

Length

2022-11-01T17:44:38.499324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0yrs168390
36.1%
0mon127976
27.4%
1yrs36650
 
7.9%
2yrs14839
 
3.2%
6mon11085
 
2.4%
1mon10117
 
2.2%
7mon9881
 
2.1%
4mon9757
 
2.1%
3mon9720
 
2.1%
2mon9682
 
2.1%
Other values (24)58211
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0305338
14.4%
r233154
11.0%
s233154
11.0%
233154
11.0%
m233154
11.0%
o233154
11.0%
n233154
11.0%
y233154
11.0%
173661
 
3.5%
224581
 
1.2%
Other values (7)80726
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398924
66.1%
Decimal Number484306
 
22.9%
Space Separator233154
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0305338
63.0%
173661
 
15.2%
224581
 
5.1%
316378
 
3.4%
412806
 
2.6%
611900
 
2.5%
511129
 
2.3%
710350
 
2.1%
89171
 
1.9%
98992
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
r233154
16.7%
s233154
16.7%
m233154
16.7%
o233154
16.7%
n233154
16.7%
y233154
16.7%
Space Separator
ValueCountFrequency (%)
233154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1398924
66.1%
Common717460
33.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0305338
42.6%
233154
32.5%
173661
 
10.3%
224581
 
3.4%
316378
 
2.3%
412806
 
1.8%
611900
 
1.7%
511129
 
1.6%
710350
 
1.4%
89171
 
1.3%
Latin
ValueCountFrequency (%)
r233154
16.7%
s233154
16.7%
m233154
16.7%
o233154
16.7%
n233154
16.7%
y233154
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2116384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0305338
14.4%
r233154
11.0%
s233154
11.0%
233154
11.0%
m233154
11.0%
o233154
11.0%
n233154
11.0%
y233154
11.0%
173661
 
3.5%
224581
 
1.2%
Other values (7)80726
 
3.8%

CREDIT.HISTORY.LENGTH
Categorical

HIGH CARDINALITY

Distinct294
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0yrs 0mon
119127 
0yrs 6mon
 
4761
2yrs 1mon
 
4745
0yrs 7mon
 
4017
2yrs 0mon
 
3833
Other values (289)
96671 

Length

Max length11
Median length9
Mean length9.091218679
Min length9

Characters and Unicode

Total characters2119654
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)< 0.1%

Sample

1st row0yrs 0mon
2nd row1yrs 11mon
3rd row0yrs 0mon
4th row1yrs 3mon
5th row0yrs 0mon

Common Values

ValueCountFrequency (%)
0yrs 0mon119127
51.1%
0yrs 6mon4761
 
2.0%
2yrs 1mon4745
 
2.0%
0yrs 7mon4017
 
1.7%
2yrs 0mon3833
 
1.6%
1yrs 0mon3389
 
1.5%
1yrs 1mon3024
 
1.3%
0yrs 11mon2627
 
1.1%
0yrs 8mon2459
 
1.1%
0yrs 9mon2403
 
1.0%
Other values (284)82769
35.5%

Length

2022-11-01T17:44:38.700287image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0yrs147802
31.7%
0mon130856
28.1%
1yrs26613
 
5.7%
2yrs22613
 
4.8%
1mon13696
 
2.9%
3yrs11898
 
2.6%
6mon11248
 
2.4%
7mon10205
 
2.2%
2mon9347
 
2.0%
11mon8977
 
1.9%
Other values (37)73053
15.7%

Most occurring characters

ValueCountFrequency (%)
0287799
13.6%
r233154
11.0%
s233154
11.0%
233154
11.0%
m233154
11.0%
o233154
11.0%
n233154
11.0%
y233154
11.0%
171642
 
3.4%
232802
 
1.5%
Other values (7)95333
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1398924
66.0%
Decimal Number487576
 
23.0%
Space Separator233154
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0287799
59.0%
171642
 
14.7%
232802
 
6.7%
321171
 
4.3%
415958
 
3.3%
614259
 
2.9%
513244
 
2.7%
712200
 
2.5%
89398
 
1.9%
99103
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
r233154
16.7%
s233154
16.7%
m233154
16.7%
o233154
16.7%
n233154
16.7%
y233154
16.7%
Space Separator
ValueCountFrequency (%)
233154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1398924
66.0%
Common720730
34.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0287799
39.9%
233154
32.3%
171642
 
9.9%
232802
 
4.6%
321171
 
2.9%
415958
 
2.2%
614259
 
2.0%
513244
 
1.8%
712200
 
1.7%
89398
 
1.3%
Latin
ValueCountFrequency (%)
r233154
16.7%
s233154
16.7%
m233154
16.7%
o233154
16.7%
n233154
16.7%
y233154
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2119654
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0287799
13.6%
r233154
11.0%
s233154
11.0%
233154
11.0%
m233154
11.0%
o233154
11.0%
n233154
11.0%
y233154
11.0%
171642
 
3.4%
232802
 
1.5%
Other values (7)95333
 
4.5%

NO.OF_INQUIRIES
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2066145123
Minimum0
Maximum36
Zeros201961
Zeros (%)86.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2022-11-01T17:44:38.901060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7064977116
Coefficient of variation (CV)3.419400233
Kurtosis131.840382
Mean0.2066145123
Median Absolute Deviation (MAD)0
Skewness7.870682833
Sum48173
Variance0.4991390164
MonotonicityNot monotonic
2022-11-01T17:44:39.071894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0201961
86.6%
122285
 
9.6%
25409
 
2.3%
31767
 
0.8%
4760
 
0.3%
5343
 
0.1%
6239
 
0.1%
7135
 
0.1%
8105
 
< 0.1%
944
 
< 0.1%
Other values (15)106
 
< 0.1%
ValueCountFrequency (%)
0201961
86.6%
122285
 
9.6%
25409
 
2.3%
31767
 
0.8%
4760
 
0.3%
5343
 
0.1%
6239
 
0.1%
7135
 
0.1%
8105
 
< 0.1%
944
 
< 0.1%
ValueCountFrequency (%)
361
 
< 0.1%
281
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%
196
< 0.1%
184
< 0.1%
174
< 0.1%
163
< 0.1%
157
< 0.1%

loan_default
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
182543 
1
50611 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters233154
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0182543
78.3%
150611
 
21.7%

Length

2022-11-01T17:44:39.256564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:44:39.419333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0182543
78.3%
150611
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0182543
78.3%
150611
 
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number233154
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0182543
78.3%
150611
 
21.7%

Most occurring scripts

ValueCountFrequency (%)
Common233154
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0182543
78.3%
150611
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII233154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0182543
78.3%
150611
 
21.7%

Interactions

2022-11-01T17:44:04.363401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:44.257357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:54.147262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:03.801151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:14.002352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:23.681888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:33.171567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:42.931920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:52.496248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:02.695132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:12.141565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:22.315715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:31.877547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:41.817611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:51.670899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:01.575673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:11.496937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:20.508892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:29.938479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:39.345366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:48.388609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:57.802951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:07.886652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:17.503107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:27.278090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:36.642038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:45.578611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:54.655369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:04.668936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:44.720859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:54.451587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:04.125690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:14.350064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:23.998345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:33.503635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:43.263981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:52.851094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:03.041701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:12.464499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:22.616504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:32.178334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:42.171812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:52.049827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:01.912266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:11.791560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:20.825307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:30.249102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:39.708675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:48.684131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:58.103792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:08.192106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:17.803089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:27.632274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:36.935211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:45.889780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:54.971811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:04.999752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:45.144052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:54.758563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:04.628326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:14.704309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:24.336917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:33.835698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:43.580413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:53.236372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:03.411539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:12.828075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:22.936471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:32.479136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:42.488268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:52.551171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:02.234875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:12.102661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:21.126738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:30.558959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:40.094118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:49.005092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:58.490552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:08.580756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:18.119541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:28.011208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:37.249401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:46.220807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:55.297537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:05.378679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:45.552313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:55.106247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:04.977467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:15.067605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:24.668959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:34.205553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:43.931777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:53.699941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:03.828257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:13.182307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:23.284166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:32.797927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:42.842457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:52.889361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:02.575193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:12.436239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:21.474415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:30.885961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:40.458705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:49.351903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:58.826184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:08.965819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:18.461499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:28.550293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:37.570733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:46.568489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:55.654635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:05.679478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:45.887004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:55.407039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:05.278260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:15.437433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:24.969771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:34.522002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:44.232599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-01T17:42:47.414091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:56.683522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:06.835834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:16.525554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:26.187785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:35.664727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:44.553868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:53.666748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:03.323762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:12.729458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:53.476576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:03.121435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:13.231432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:23.017769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:32.401106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:42.245590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:51.806007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:01.993237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:11.461820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:21.598866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:31.160053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:41.084497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:50.831005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:00.935997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:10.823176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:19.838947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:29.279708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:38.584314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:47.736573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:57.007735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:07.183507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:16.839822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:26.518373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:35.981148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:44.861139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:53.983191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:03.655401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:13.065977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:39:53.830806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:03.469100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:13.601266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:23.365461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:32.732797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:42.599835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:40:52.164192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:02.347462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:11.809507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:21.968027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:31.545508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:41.454307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:41:51.285470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:01.258917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:11.164883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:20.178978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:29.611757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:38.906540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:48.061583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:42:57.451721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:07.537723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:17.171043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:26.861389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:36.326681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:45.262157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:43:54.339317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:44:04.012897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-01T17:44:39.704679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-01T17:44:40.435847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-01T17:44:41.131020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-01T17:44:41.914844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-01T17:44:42.728936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-01T17:44:43.170662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-01T17:44:14.005834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-01T17:44:18.244126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-01T17:44:21.156110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

UniqueIDdisbursed_amountasset_costltvbranch_idsupplier_idmanufacturer_idCurrent_pincode_IDDate.of.BirthEmployment.TypeDisbursalDateState_IDEmployee_code_IDMobileNo_Avl_FlagAadhar_flagPAN_flagVoterID_flagDriving_flagPassport_flagPERFORM_CNS.SCOREPERFORM_CNS.SCORE.DESCRIPTIONPRI.NO.OF.ACCTSPRI.ACTIVE.ACCTSPRI.OVERDUE.ACCTSPRI.CURRENT.BALANCEPRI.SANCTIONED.AMOUNTPRI.DISBURSED.AMOUNTSEC.NO.OF.ACCTSSEC.ACTIVE.ACCTSSEC.OVERDUE.ACCTSSEC.CURRENT.BALANCESEC.SANCTIONED.AMOUNTSEC.DISBURSED.AMOUNTPRIMARY.INSTAL.AMTSEC.INSTAL.AMTNEW.ACCTS.IN.LAST.SIX.MONTHSDELINQUENT.ACCTS.IN.LAST.SIX.MONTHSAVERAGE.ACCT.AGECREDIT.HISTORY.LENGTHNO.OF_INQUIRIESloan_default
0420825505785840089.55672280745144101-01-84Salaried03-08-18619981100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
1537409471456555073.23672280745150231-07-85Self employed26-09-1861998110000598I-Medium Risk11127600502005020000000019910011yrs 11mon1yrs 11mon01
2417566532786136089.63672280745149724-08-85Self employed01-08-18619981100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
3624493575136611388.48672280745150130-12-93Self employed26-10-1861998110000305L-Very High Risk300000000000310000yrs 8mon1yrs 3mon11
4539055523786030088.39672280745149509-12-77Self employed26-09-18619981100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon11
5518279545136190089.66672280745150108-09-90Self employed19-09-1861998110000825A-Very Low Risk20000000000013470001yrs 9mon2yrs 0mon00
6529269463496150076.42672280745150201-06-88Salaried23-09-18619981100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
7510278438946190071.89672280745150104-10-89Salaried16-09-186199811000017Not Scored: Not Enough Info available on the customer11072879745007450000000000000yrs 2mon0yrs 2mon00
8490213537136197389.56672280745149715-11-91Self employed05-09-1861998110000718D-Very Low Risk110-4136538436538400000000004yrs 8mon4yrs 8mon10
9510980526036130086.95672280745149201-06-68Salaried16-09-1861998100100818A-Very Low Risk10000000000026080001yrs 7mon1yrs 7mon00

Last rows

UniqueIDdisbursed_amountasset_costltvbranch_idsupplier_idmanufacturer_idCurrent_pincode_IDDate.of.BirthEmployment.TypeDisbursalDateState_IDEmployee_code_IDMobileNo_Avl_FlagAadhar_flagPAN_flagVoterID_flagDriving_flagPassport_flagPERFORM_CNS.SCOREPERFORM_CNS.SCORE.DESCRIPTIONPRI.NO.OF.ACCTSPRI.ACTIVE.ACCTSPRI.OVERDUE.ACCTSPRI.CURRENT.BALANCEPRI.SANCTIONED.AMOUNTPRI.DISBURSED.AMOUNTSEC.NO.OF.ACCTSSEC.ACTIVE.ACCTSSEC.OVERDUE.ACCTSSEC.CURRENT.BALANCESEC.SANCTIONED.AMOUNTSEC.DISBURSED.AMOUNTPRIMARY.INSTAL.AMTSEC.INSTAL.AMTNEW.ACCTS.IN.LAST.SIX.MONTHSDELINQUENT.ACCTS.IN.LAST.SIX.MONTHSAVERAGE.ACCT.AGECREDIT.HISTORY.LENGTHNO.OF_INQUIRIESloan_default
233144613161560596900183.04342302486104415-06-63Salaried24-10-18637051100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
233145606146498036697376.15342108145105123-12-85Self employed23-10-1863705110000690E-Low Risk74013064856298022600000016720200yrs 9mon2yrs 6mon10
233146622612384395296574.58342070048105123-07-82Self employed26-10-1863705110000738C-Very Low Risk2207001148391483900000000200yrs 3mon0yrs 3mon00
2331476456977262310540569.73342070048105119-06-89Salaried31-10-1863705110000755C-Very Low Risk44020142227662423797700000000100yrs 9mon1yrs 0mon00
233148613494428946033472.93342070048105108-07-93Salaried24-10-18637051100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
2331496264326321310540560.72342070048105001-08-88Salaried26-10-1863705100100735D-Very Low Risk43039044341613341613300000040840001yrs 9mon3yrs 3mon00
2331506061417365110060074.9534237755199005-12-88Self employed23-10-1863705100100825A-Very Low Risk10000000000015650000yrs 6mon0yrs 6mon00
233151613658334847121248.45772218686229901-06-76Salaried24-10-18434791100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
233152548084342597328649.10772218686229926-03-94Salaried29-09-18434791100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
2331536302137575111600966.81772218686229918-02-84Salaried27-10-18434791100000No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00